Applied Scientist vs. Research Engineer

Applied Scientist vs Research Engineer: A Comprehensive Comparison

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The fields of Artificial Intelligence (AI), Machine Learning (ML), and Big Data are rapidly growing, and with this growth comes a demand for professionals who can develop and apply these technologies to solve real-world problems. Two roles that are often confused in this space are Applied Scientist and Research Engineer. While there is some overlap in their responsibilities and required skills, there are also distinct differences between the two roles. In this article, we will provide a detailed comparison of these two roles, including their definitions, responsibilities, required skills, educational backgrounds, tools and software used, common industries, outlooks, and practical tips for getting started in these careers.

Definitions

An Applied Scientist is a professional who applies scientific principles, theories, and methods to solve practical problems in the field of AI/ML. They work on developing and implementing algorithms, models, and systems that can be used to solve real-world problems. Applied Scientists typically work in industry and are focused on creating practical solutions that can be implemented in products or services.

On the other hand, a Research Engineer is a professional who works on developing new technologies and advancing the state-of-the-art in the field of AI/ML. They are typically employed in research labs, academic institutions, or large technology companies, and their focus is on developing new algorithms, models, and systems that can be used to solve complex problems.

Responsibilities

The responsibilities of an Applied Scientist and a Research Engineer can vary depending on the organization they work for. However, there are some general responsibilities that are associated with each role.

Applied Scientist

• Develop and implement algorithms, models, and systems that solve practical problems
• Work with data scientists and engineers to ensure that solutions are scalable and efficient
• Collaborate with product managers to understand customer needs and develop solutions that meet those needs
• Stay up-to-date with the latest research in the field and apply those findings to practical problems
• Evaluate and test new technologies and methods to determine their effectiveness

Research Engineer

• Develop new algorithms, models, and systems that advance the state-of-the-art in the field
• Work on research projects that may not have immediate practical applications
• Collaborate with other researchers to develop new ideas and approaches
• Publish research papers and attend conferences to share findings with the wider research community
• Evaluate and test new technologies and methods to determine their effectiveness

Required Skills

Both Applied Scientists and Research Engineers require a strong foundation in Mathematics, Statistics, and Computer Science. However, there are some specific skills that are more important for each role.

Applied Scientist

• Strong programming skills in languages such as Python, R, or Java
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn
• Knowledge of statistics and Probability theory
• Ability to work with large datasets and databases
• Familiarity with software development practices such as version control and Testing

Research Engineer

• Strong programming skills in languages such as C++, Python, or Java
• Experience with Deep Learning frameworks such as TensorFlow, PyTorch, or Caffe
• Knowledge of advanced mathematics such as Linear algebra and calculus
• Familiarity with Computer Vision or natural language processing
• Ability to develop and implement new algorithms and models

Educational Backgrounds

Both Applied Scientists and Research Engineers typically have advanced degrees in Computer Science, mathematics, statistics, or a related field. However, there are some differences in the types of degrees that are most common for each role.

Applied Scientist

• Master's or PhD in computer science, statistics, or a related field
• Experience with practical applications of Machine Learning and data science
• Strong programming skills

Research Engineer

• PhD in computer science, Mathematics, or a related field
• Experience with advanced research topics in AI/ML
• Strong mathematical and theoretical skills

Tools and Software Used

Both Applied Scientists and Research Engineers use a variety of tools and software in their work. However, there are some specific tools and software that are more commonly used by each role.

Applied Scientist

• Machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn
• Data analysis and visualization tools such as Pandas, Matplotlib, or Tableau
• Cloud computing platforms such as AWS, Azure, or Google Cloud
• Software development tools such as Git, Jenkins, or Docker

Research Engineer

• Deep learning frameworks such as TensorFlow, PyTorch, or Caffe
• Research tools such as Matlab, Mathematica, or Octave
• High-performance computing tools such as MPI or OpenMP
• Programming languages such as C++, Python, or Julia

Common Industries

Both Applied Scientists and Research Engineers are in high demand across a variety of industries. However, there are some industries that are more likely to employ each role.

Applied Scientist

• Technology companies such as Google, Microsoft, or Amazon
• Healthcare companies such as Pfizer, Roche, or Novartis
• Financial services companies such as JPMorgan Chase, Goldman Sachs, or BlackRock
• Retail companies such as Walmart, Target, or Amazon

Research Engineer

• Academic institutions such as universities or research labs
• Large technology companies such as Google, Microsoft, or IBM
• Government agencies such as NASA or DARPA
• Defense contractors such as Lockheed Martin or Raytheon

Outlooks

The outlook for both Applied Scientists and Research Engineers is very positive, with strong demand for both roles expected to continue in the coming years. According to the Bureau of Labor Statistics, employment of computer and information research scientists (which includes both Applied Scientists and Research Engineers) is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations.

Practical Tips for Getting Started

If you are interested in pursuing a career as an Applied Scientist or Research Engineer, there are some practical tips that can help you get started.

Applied Scientist

• Gain experience with practical applications of machine learning and data science through internships or personal projects
• Develop strong programming skills in Python, R, or Java
• Build a portfolio of projects that demonstrate your skills and experience
• Network with professionals in the field through conferences or online communities

Research Engineer

• Pursue a PhD in computer science, mathematics, or a related field
• Gain experience with advanced research topics in AI/ML through internships or research assistantships
• Publish research papers and attend conferences to build your reputation in the field
• Network with other researchers in the field through conferences or online communities

Conclusion

In conclusion, while Applied Scientists and Research Engineers share some similarities in their work, there are also distinct differences between the two roles. Applied Scientists focus on developing practical solutions to real-world problems, while Research Engineers focus on advancing the state-of-the-art in the field. Both roles require strong skills in mathematics, statistics, and computer science, but there are also specific skills and tools that are more important for each role. Regardless of which role you choose, there is a strong demand for professionals in the AI/ML and Big Data space, and pursuing a career in this field can be both challenging and rewarding.